Evidence receipt / preference
Published · transcript-backedShawn Wang: preference
16 Aug 2023 Latent Space The Mathematics of Training LLMs — with Quentin Anthony of Eleuther AI
“It matters that you are going for the sort of good enough rules of thumb, because I think a lot of people try to go for precision and being overly precise actually is not helpful.”
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- Speaker
- Shawn Wang
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- Claim type
- preference
- Recorded
- 16 Aug 2023
- Publisher
- Latent Space
Transcript context
…I would say Bloom. So the Hugging Face Bloom project in big science and all of that, that was very open. I'd say it's the same caliber, if not more detailed than OPT. Other than that, I think there was like a doc from Microsoft on like their Turing NLG. Their paper is pretty relaxed in that it did talk about some of those challenges. Other than like OPT and Bloom and us, I can't think of any. It's a new thing. It matters that you are going for the sort of good enough rules of thumb, because I think a lot of people try to go for precision and being overly precise actually is not helpful. Right. Yes. You'll see some like statements in the blog posts that are just like, we think this is about 1.2 in our experience. And, you know, we don't go any further into detail and it would take maybe an extra month for us to chase down every single little piece of memory. But instead, like getting good enough is still helpful to people.…
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